Method and system for providing analyte monitoring

- Abbott Diabetes Care Inc.

Methods and apparatuses for determining an analyte value are disclosed.

Skip to: Description  ·  Claims  ·  References Cited  · Patent History  ·  Patent History
Description
RELATED APPLICATIONS

The present application is a divisional of pending U.S. patent application Ser. No. 11/552,935 filed Oct. 25, 2006, entitled “Method and System for Providing Analyte Monitoring,” the disclosure of which is incorporated herein by reference for all purposes.

BACKGROUND

Analyte, e.g., glucose monitoring systems including continuous and discrete monitoring systems generally include a small, lightweight battery powered and microprocessor controlled system which is configured to detect signals proportional to the corresponding measured glucose levels using an electrometer, and RF signals to transmit the collected data. One aspect of certain analyte monitoring systems include a transcutaneous or subcutaneous analyte sensor configuration which is, for example, partially mounted on the skin of a subject whose analyte level is to be monitored. The sensor cell may use a two or three-electrode (work, reference and counter electrodes) configuration driven by a controlled potential (potentiostat) analog circuit connected through a contact system.

The analyte sensor may be configured so that a portion thereof is placed under the skin of the patient so as to detect the analyte levels of the patient, and another segment of the analyte sensor that is in communication with the transmitter unit. The transmitter unit is configured to transmit the analyte levels detected by the sensor over a wireless communication link such as an RF (radio frequency) communication link to a receiver/monitor unit. The receiver/monitor unit performs data analysis, among others on the received analyte levels to generate information pertaining to the monitored analyte levels.

To obtain accurate data from the analyte sensor, calibration using capillary blood glucose measurements is necessary. Typically, blood glucose measurements are obtained using, for example, a blood glucose meter, and the measured blood glucose values are used to calibrate the sensors. Due to a lag factor between the monitored sensor data and the measured blood glucose values, an error, or signal noise such as signal dropouts, is typically introduced in calibration using the monitored data as well as in computing the displayed glucose value. While correcting for the lag factors can minimize the error due to lag in the presence of noise, in the presence of signal dropouts, such error compensation may reduce accuracy of the monitored sensor data.

In view of the foregoing, it would be desirable to have a method and system for providing noise filtering and signal dropout detection and/or compensation in data monitoring systems.

SUMMARY OF THE INVENTION

In one embodiment, a method for minimizing the effect of noise and signal dropouts in a glucose sensor including monitoring a data stream, generating a noise-filtered signal associated with the data stream, determining a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout are disclosed.

These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description of the embodiments, the appended claims and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a data monitoring and management system for practicing one or more embodiments of the present invention;

FIG. 2 is a block diagram of the transmitter unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention;

FIG. 3 is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention;

FIG. 4 is a functional diagram of the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention;

FIG. 5 is a flowchart illustrating the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention;

FIG. 6 is a flowchart illustrating the process input estimation in accordance with one embodiment of the present invention;

FIG. 7 is a flowchart illustrating the noise filtered estimation;

FIG. 8 is a flowchart illustrating signal dropout detection in accordance with one embodiment of the present invention;

FIG. 9 is a flowchart illustrating an overall signal dropout compensation in accordance with one embodiment of the present invention; and

FIG. 10 is flowchart illustrating a detailed signal dropout compensation determination of FIG. 9 in accordance with one embodiment of the present invention.

 DETAILED DESCRIPTION

As described in further detail below, in accordance with the various embodiments of the present invention, there is provided a method and system for providing noise filtered and/or signal dropout mitigated processes for signals in analyte monitoring systems. In particular, within the scope of the present invention, there are provided method and system for noise filtering, signal dropout detection, and signal dropout compensation to improve the accuracy of lag compensation.

FIG. 1 illustrates a data monitoring and management system such as, for example, analyte (e.g., glucose) monitoring system 100 in accordance with one embodiment of the present invention. The subject invention is further described primarily with respect to a glucose monitoring system for convenience and such description is in no way intended to limit the scope of the invention. It is to be understood that the analyte monitoring system may be configured to monitor a variety of analytes, e.g., lactate, and the like.

Analytes that may be monitored include, for example, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketones, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin. The concentration of drugs, such as, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored.

The analyte monitoring system 100 includes a sensor 101, a transmitter unit 102 coupled to the sensor 101, and a primary receiver unit 104 which is configured to communicate with the transmitter unit 102 via a communication link 103. The primary receiver unit 104 may be further configured to transmit data to a data processing terminal 105 for evaluating the data received by the primary receiver unit 104. Moreover, the data processing terminal in one embodiment may be configured to receive data directly from the transmitter unit 102 via a communication link 106 which may optionally be configured for bi-directional communication.

Also shown in FIG. 1 is a secondary receiver unit 106 which is operatively coupled to the communication link and configured to receive data transmitted from the transmitter unit 102. Moreover, as shown in the Figure, the secondary receiver unit 106 is configured to communicate with the primary receiver unit 104 as well as the data processing terminal 105. Indeed, the secondary receiver unit 106 may be configured for bi-directional wireless communication with each of the primary receiver unit 104 and the data processing terminal 105. As discussed in further detail below, in one embodiment of the present invention, the secondary receiver unit 106 may be configured to include a limited number of functions and features as compared with the primary receiver unit 104. As such, the secondary receiver unit 106 may be configured substantially in a smaller compact housing or embodied in a device such as a wrist watch, for example. Alternatively, the secondary receiver unit 106 may be configured with the same or substantially similar functionality as the primary receiver unit 104, and may be configured to be used in conjunction with a docking cradle unit for placement by bedside, for night time monitoring, and/or bi-directional communication device.

Only one sensor 101, transmitter unit 102, communication link 103, and data processing terminal 105 are shown in the embodiment of the analyte monitoring system 100 illustrated in FIG. 1. However, it will be appreciated by one of ordinary skill in the art that the analyte monitoring system 100 may include one or more sensor 101, transmitter unit 102, communication link 103, and data processing terminal 105. Moreover, within the scope of the present invention, the analyte monitoring system 100 may be a continuous monitoring system, or semi-continuous, or a discrete monitoring system. In a multi-component environment, each device is configured to be uniquely identified by each of the other devices in the system so that communication conflict is readily resolved between the various components within the analyte monitoring system 100.

In one embodiment of the present invention, the sensor 101 is physically positioned in or on the body of a user whose analyte level is being monitored. The sensor 101 may be configured to continuously sample the analyte level of the user and convert the sampled analyte level into a corresponding data signal for transmission by the transmitter unit 102. In one embodiment, the transmitter unit 102 is mounted on the sensor 101 so that both devices are positioned on the user's body. The transmitter unit 102 performs data processing such as filtering and encoding on data signals, each of which corresponds to a sampled analyte level of the user, for transmission to the primary receiver unit 104 via the communication link 103.

In one embodiment, the analyte monitoring system 100 is configured as a one-way RF communication path from the transmitter unit 102 to the primary receiver unit 104. In such embodiment, the transmitter unit 102 transmits the sampled data signals received from the sensor 101 without acknowledgement from the primary receiver unit 104 that the transmitted sampled data signals have been received. For example, the transmitter unit 102 may be configured to transmit the encoded sampled data signals at a fixed rate (e.g., at one minute intervals) after the completion of the initial power on procedure. Likewise, the primary receiver unit 104 may be configured to detect such transmitted encoded sampled data signals at predetermined time intervals. Alternatively, the analyte monitoring system 100 may be configured with a bi-directional RF (or otherwise) communication between the transmitter unit 102 and the primary receiver unit 104.

Additionally, in one aspect, the primary receiver unit 104 may include two sections. The first section is an analog interface section that is configured to communicate with the transmitter unit 102 via the communication link 103. In one embodiment, the analog interface section may include an RF receiver and an antenna for receiving and amplifying the data signals from the transmitter unit 102, which are thereafter, demodulated with a local oscillator and filtered through a band-pass filter. The second section of the primary receiver unit 104 is a data processing section which is configured to process the data signals received from the transmitter unit 102 such as by performing data decoding, error detection and correction, data clock generation, and data bit recovery.

In operation, upon completing the power-on procedure, the primary receiver unit 104 is configured to detect the presence of the transmitter unit 102 within its range based on, for example, the strength of the detected data signals received from the transmitter unit 102 or a predetermined transmitter identification information. Upon successful synchronization with the corresponding transmitter unit 102, the primary receiver unit 104 is configured to begin receiving from the transmitter unit 102 data signals corresponding to the user's detected analyte level. More specifically, the primary receiver unit 104 in one embodiment is configured to perform synchronized time hopping with the corresponding synchronized transmitter unit 102 via the communication link 103 to obtain the user's detected analyte level.

Referring again to FIG. 1, the data processing terminal 105 may include a personal computer, a portable computer such as a laptop or a handheld device (e.g., personal digital assistants (PDAs)), and the like, each of which may be configured for data communication with the receiver via a wired or a wireless connection. Additionally, the data processing terminal 105 may further be connected to a data network (not shown) for storing, retrieving and updating data corresponding to the detected analyte level of the user.

Within the scope of the present invention, the data processing terminal 105 may include an infusion device such as an insulin infusion pump or the like, which may be configured to administer insulin to patients, and which may be configured to communicate with the receiver unit 104 for receiving, among others, the measured analyte level. Alternatively, the receiver unit 104 may be configured to integrate an infusion device therein so that the receiver unit 104 is configured to administer insulin therapy to patients, for example, for administering and modifying basal profiles, as well as for determining appropriate boluses for administration based on, among others, the detected analyte levels received from the transmitter unit 102.

Additionally, the transmitter unit 102, the primary receiver unit 104 and the data processing terminal 105 may each be configured for bi-directional wireless communication such that each of the transmitter unit 102, the primary receiver unit 104 and the data processing terminal 105 may be configured to communicate (that is, transmit data to and receive data from) with each other via the wireless communication link 103. More specifically, the data processing terminal 105 may in one embodiment be configured to receive data directly from the transmitter unit 102 via the communication link 106, where the communication link 106, as described above, may be configured for bi-directional communication.

In this embodiment, the data processing terminal 105 which may include an insulin pump, may be configured to receive the analyte signals from the transmitter unit 102, and thus, incorporate the functions of the receiver 103 including data processing for managing the patient's insulin therapy and analyte monitoring. In one embodiment, the communication link 103 may include one or more of an RF communication protocol, an infrared communication protocol, a Bluetooth enabled communication protocol, an 802.11x wireless communication protocol, or an equivalent wireless communication protocol which would allow secure, wireless communication of several units (for example, per HIPPA requirements) while avoiding potential data collision and interference.

FIG. 2 is a block diagram of the transmitter of the data monitoring and detection system shown in FIG. 1 in accordance with one embodiment of the present invention. Referring to the Figure, the transmitter unit 102 in one embodiment includes an analog interface 201 configured to communicate with the sensor 101 (FIG. 1), a user input 202, and a temperature detection section 203, each of which is operatively coupled to a transmitter processor 204 such as a central processing unit (CPU). As can be seen from FIG. 2, there are provided four contacts, three of which are electrodes—work electrode (W) 210, guard contact (G) 211, reference electrode (R) 212, and counter electrode (C) 213, each operatively coupled to the analog interface 201 of the transmitter unit 102 for connection to the sensor unit 201 (FIG. 1). In one embodiment, each of the work electrode (W) 210, guard contact (G) 211, reference electrode (R) 212, and counter electrode (C) 213 may be made using a conductive material that is either printed or etched, for example, such as carbon which may be printed, or metal foil (e.g., gold) which may be etched.

Further shown in FIG. 2 are a transmitter serial communication section 205 and an RF transmitter 206, each of which is also operatively coupled to the transmitter processor 204. Moreover, a power supply 207 such as a battery is also provided in the transmitter unit 102 to provide the necessary power for the transmitter unit 102. Additionally, as can be seen from the Figure, clock 208 is provided to, among others, supply real time information to the transmitter processor 204.

In one embodiment, a unidirectional input path is established from the sensor 101 (FIG. 1) and/or manufacturing and testing equipment to the analog interface 201 of the transmitter unit 102, while a unidirectional output is established from the output of the RF transmitter 206 of the transmitter unit 102 for transmission to the primary receiver unit 104. In this manner, a data path is shown in FIG. 2 between the aforementioned unidirectional input and output via a dedicated link 209 from the analog interface 201 to serial communication section 205, thereafter to the processor 204, and then to the RF transmitter 206. As such, in one embodiment, via the data path described above, the transmitter unit 102 is configured to transmit to the primary receiver unit 104 (FIG. 1), via the communication link 103 (FIG. 1), processed and encoded data signals received from the sensor 101 (FIG. 1). Additionally, the unidirectional communication data path between the analog interface 201 and the RF transmitter 206 discussed above allows for the configuration of the transmitter unit 102 for operation upon completion of the manufacturing process as well as for direct communication for diagnostic and testing purposes.

As discussed above, the transmitter processor 204 is configured to transmit control signals to the various sections of the transmitter unit 102 during the operation of the transmitter unit 102. In one embodiment, the transmitter processor 204 also includes a memory (not shown) for storing data such as the identification information for the transmitter unit 102, as well as the data signals received from the sensor 101. The stored information may be retrieved and processed for transmission to the primary receiver unit 104 under the control of the transmitter processor 204. Furthermore, the power supply 207 may include a commercially available battery.

The transmitter unit 102 is also configured such that the power supply section 207 is capable of providing power to the transmitter for a minimum of about three months of continuous operation after having been stored for about eighteen months in a low-power (non-operating) mode. In one embodiment, this may be achieved by the transmitter processor 204 operating in low power modes in the non-operating state, for example, drawing no more than approximately 1 μA of current. Indeed, in one embodiment, the final step during the manufacturing process of the transmitter unit 102 may place the transmitter unit 102 in the lower power, non-operating state (i.e., post-manufacture sleep mode). In this manner, the shelf life of the transmitter unit 102 may be significantly improved. Moreover, as shown in FIG. 2, while the power supply unit 207 is shown as coupled to the processor 204, and as such, the processor 204 is configured to provide control of the power supply unit 207, it should be noted that within the scope of the present invention, the power supply unit 207 is configured to provide the necessary power to each of the components of the transmitter unit 102 shown in FIG. 2.

Referring back to FIG. 2, the power supply section 207 of the transmitter unit 102 in one embodiment may include a rechargeable battery unit that may be recharged by a separate power supply recharging unit (for example, provided in the receiver unit 104) so that the transmitter unit 102 may be powered for a longer period of usage time. Moreover, in one embodiment, the transmitter unit 102 may be configured without a battery in the power supply section 207, in which case the transmitter unit 102 may be configured to receive power from an external power supply source (for example, a battery) as discussed in further detail below.

Referring yet again to FIG. 2, the temperature detection section 203 of the transmitter unit 102 is configured to monitor the temperature of the skin near the sensor insertion site. The temperature reading is used to adjust the analyte readings obtained from the analog interface 201. The RF transmitter 206 of the transmitter unit 102 may be configured for operation in the frequency band of 315 MHz to 322 MHz, for example, in the United States. Further, in one embodiment, the RF transmitter 206 is configured to modulate the carrier frequency by performing Frequency Shift Keying and Manchester encoding. In one embodiment, the data transmission rate is 19,200 symbols per second, with a minimum transmission range for communication with the primary receiver unit 104.

Referring yet again to FIG. 2, also shown is a leak detection circuit 214 coupled to the guard electrode (G) 211 and the processor 204 in the transmitter unit 102 of the data monitoring and management system 100. The leak detection circuit 214 in accordance with one embodiment of the present invention may be configured to detect leakage current in the sensor 101 to determine whether the measured sensor data are corrupt or whether the measured data from the sensor 101 is accurate.

Additional detailed description of the continuous analyte monitoring system, its various components including the functional descriptions of the transmitter are provided in U.S. Pat. No. 6,175,752 issued Jan. 16, 2001 entitled “Analyte Monitoring Device and Methods of Use”, and in application Ser. No. 10/745,878 filed Dec. 26, 2003 entitled “Continuous Glucose Monitoring System and Methods of Use”, each assigned to the Assignee of the present application.

FIG. 3 is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention. Referring to FIG. 3, the primary receiver unit 104 includes a blood glucose test strip interface 301, an RF receiver 302, an input 303, a temperature detection section 304, and a clock 305, each of which is operatively coupled to a receiver processor 307. As can be further seen from the Figure, the primary receiver unit 104 also includes a power supply 306 operatively coupled to a power conversion and monitoring section 308. Further, the power conversion and monitoring section 308 is also coupled to the receiver processor 307. Moreover, also shown are a receiver serial communication section 309, and an output 310, each operatively coupled to the receiver processor 307.

In one embodiment, the test strip interface 301 includes a glucose level testing portion to receive a manual insertion of a glucose test strip, and thereby determine and display the glucose level of the test strip on the output 310 of the primary receiver unit 104. This manual testing of glucose can be used to calibrate sensor 101. The RF receiver 302 is configured to communicate, via the communication link 103 (FIG. 1) with the RF transmitter 206 of the transmitter unit 102, to receive encoded data signals from the transmitter unit 102 for, among others, signal mixing, demodulation, and other data processing. The input 303 of the primary receiver unit 104 is configured to allow the user to enter information into the primary receiver unit 104 as needed. In one aspect, the input 303 may include one or more keys of a keypad, a touch-sensitive screen, or a voice-activated input command unit. The temperature detection section 304 is configured to provide temperature information of the primary receiver unit 104 to the receiver processor 307, while the clock 305 provides, among others, real time information to the receiver processor 307.

Each of the various components of the primary receiver unit 104 shown in FIG. 3 is powered by the power supply 306 which, in one embodiment, includes a battery. Furthermore, the power conversion and monitoring section 308 is configured to monitor the power usage by the various components in the primary receiver unit 104 for effective power management and to alert the user, for example, in the event of power usage which renders the primary receiver unit 104 in sub-optimal operating conditions. An example of such sub-optimal operating condition may include, for example, operating the vibration output mode (as discussed below) for a period of time thus substantially draining the power supply 306 while the processor 307 (thus, the primary receiver unit 104) is turned on. Moreover, the power conversion and monitoring section 308 may additionally be configured to include a reverse polarity protection circuit such as a field effect transistor (FET) configured as a battery activated switch.

The serial communication section 309 in the primary receiver unit 104 is configured to provide a bi-directional communication path from the testing and/or manufacturing equipment for, among others, initialization, testing, and configuration of the primary receiver unit 104. Serial communication section 104 can also be used to upload data to a computer, such as time-stamped blood glucose data. The communication link with an external device (not shown) can be made, for example, by cable, infrared (IR) or RF link. The output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information. Additionally, the output 310 may also include an integrated speaker for outputting audible signals as well as to provide vibration output as commonly found in handheld electronic devices, such as mobile telephones presently available. In a further embodiment, the primary receiver unit 104 also includes an electro-luminescent lamp configured to provide backlighting to the output 310 for output visual display in dark ambient surroundings.

Referring back to FIG. 3, the primary receiver unit 104 in one embodiment may also include a storage section such as a programmable, non-volatile memory device as part of the processor 307, or provided separately in the primary receiver unit 104, operatively coupled to the processor 307. The processor 307 is further configured to perform Manchester decoding as well as error detection and correction upon the encoded data signals received from the transmitter unit 102 via the communication link 103.

FIG. 4 is a functional diagram of the overall signal processing for noise filtering and signal dropout compensation, while FIG. 5 shows a flowchart illustrating the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention. Referring to the Figures, in one embodiment, signals measured are received from, for example, the analyte sensor 101 (FIG. 1) and are provided to the state observer 410 which in one embodiment may be configured to provide prior or past noise filtered estimate to a process input estimator 420.

In one embodiment, the process input estimator 420 may be configured to generate a process input estimate based on the prior or past noise filtered estimate of the received or measured signal (510), which is then provided to the state observer 410. In one aspect, and as described in further detail below in conjunction with FIG. 6, the process input estimate at a predetermined time t may be based on past noise filtered estimate of the signal.

Thereafter, in one embodiment, the state observer 410 may be configured to generate a noise filtered estimate of the measured or received signal based on the current measured or received signal and the process input estimate (520) received from the process input estimator 420. In one embodiment and as described in further detail below in conjunction with FIG. 7, using the real time process input and sensor measurement signals, a noise filtered estimate of the signal at the latest time t may be determined.

In one aspect, this routine of generating the process input estimate based on the past noise filtered estimate of the received or measured signal, and generating the noise filtered estimate of the signal based on the current received or measured signal and the current determined or generated process input estimate may be repeated for each measurement signal received, for example, from the analyte sensor 101 (FIG. 1). In this manner, in one aspect, the noise filtered signals corresponding to the measured or received sensor signals may be determined.

Referring back to FIGS. 4 and 5, in one embodiment, with the noise filtered estimate, the presence of signal dropouts are detected based on, for example, the current and past noise filtered estimate of the received or measured signal (530). More specifically, in one embodiment, a dropout detector 430 may be configured to detect signal dropouts, and thereafter, detection of signal dropouts are provided to dropout compensator 440. In one aspect, the dropout detector 430 may be configured to generate a signal or notification associated with the detection of a signal dropout (as shown in FIG. 4). That is, in one embodiment and as described in further detail below in conjunction with FIG. 8, the dropout detector 430 may be configured to detect or estimate the presence or absence of signal dropouts at the predetermined time.

In one embodiment, the dropout compensator 440 may be configured to generate an estimate of the noise filtered, dropout compensated signal (540) when the signal dropout is detected (for example, by the dropout detector 430), by subtracting the estimate of the current dropout signal source from the present noise filtered estimate of the signal. In this manner, and as described in further detail below in conjunction with FIGS. 9-10, in one embodiment of the present invention, the noise filtered signal dropout mitigated or compensated signal may be generated to improve accuracy of the measured or received signal from, for example, the analyte sensor 101 (FIG. 1).

FIG. 6 is a flowchart illustrating the process input estimation in accordance with one embodiment of the present invention. Referring to FIG. 6, a mean component of the process input estimate um(t) based on past noise filtered estimate of the signal is generated (610). For example, in one embodiment, a series of five past noise-filtered estimate of the signal, xi(t−5), xi(t−4), xi(t−3), xi(t−2), xi(t−1), the mean component of the process input estimate at time t, um(t) may be determined by taking the unweighted average of these signals as shown by the following relationship:

u m ⁡ ( t ) = x i ⁡ ( t - 5 ) + x i ⁡ ( t - 4 ) + x i ⁡ ( t - 3 ) + x i ⁡ ( t - 2 ) + x i ⁡ ( t - 1 ) 5 ( 1 )

Alternatively, the mean component of the process input estimate at time t may be determined by taking the weighted average of these signals as shown by the following relationship:

u m ⁡ ( t ) = a 5 ⁢ x i ⁡ ( t - 5 ) + a 4 ⁢ x i ⁢ ( t - 4 ) + a 3 ⁢ x i ⁡ ( t - 3 ) + a 2 ⁢ x i ⁡ ( t - 2 ) + a 1 ⁢ x i ⁡ ( t - 1 ) a 5 + a 4 + a 3 + a 2 + a 1 ( 2 )

where the determination of the constants a1, a2, a3, a4, a5, may be obtained based on empirical or analytical analysis of the analyte monitoring system.

In yet another embodiment, the mean component of the process input estimate at time t based on recent past data may be determined using filtering techniques, such as, but not limited to FIR filters.

Referring to FIG. 6, with the mean component of the process input estimate um(t) based on past noise filtered estimate of the signal determined, the difference component of the process input estimate at any time t, ud(t), may be generated (620) by, for example, taking an averaged difference of a series of noise-filtered estimate of the signal from the recent past. In one aspect, an unweighted average of the last three past differences may be used in the following manner:

u d ⁡ ( t ) = ( x i ⁡ ( t - 4 ) - x i ⁡ ( t - 3 ) ) + ( x i ⁢ ( t - 3 ) - x i ⁡ ( t - 2 ) ) + ( x i ⁡ ( t - 2 ) - x i ⁡ ( t - 1 ) ) 3 ( 3 )

Within the scope of the present invention, other approaches such as the use of FIR filter to determine the proper number of recent past values of xi as well as the weighting of each difference may be used.

Referring again to FIG. 6, after determining the difference component of the process input estimate at any time t, ud(t), the difference gain at any time t, Kd(t), is determined (630), for example, by using past noise-filtered estimate of the signal, xi, and/or the derived signals from xi. For example, in one embodiment, a band-limited rate xi—bandRate and a band-limited acceleration xi—bandAcc may be determined at any time t, based solely on recent past values of xi. Using the knowledge of how the amount of ud would contribute to the total process input u at any time t relates to these two variables xi—bandRate and xi—bandAcc, a functional relationship may be determined to ascertain the value of the difference gain Kd at any time t.

Alternatively, a lookup table can be constructed that determines the value of the difference gain Kd given the values of xi—bandRate and xi—bandAcc as shown below:

K d = { 2 if ( x i_bandRate > 0 ) & ⁢ ⁢ ( x i_bandAcc > 0 ) 1 if ( x i_bandRate > 0 ) & ⁢ ⁢ ( x i_bandAcc ≤ 0 ) 1 if ( x i_bandRate ≤ 0 ) & ⁢ ⁢ ( x i_bandAcc ≤ 0 ) 0.5 if ( x i_bandRate ≤ 0 ) & ⁢ ⁢ ( x i_bandAcc > 0 ) ( 4 )

In one aspect, the difference gain Kd may be used to scale the contribution of the difference component of the process input estimate ud in the value of the process input estimate at a given time. For example, a relatively larger value of the difference gain Kd may indicate a larger contribution of the difference component of the process input estimate ud in the value of the process input estimate at the particular time, and so on. In this manner, in one aspect, the lookup table may show the relationship between factors such as the band-limited rate xi—bandRate and the band-limited acceleration xi—bandAcc upon how much the difference component of the process input estimate ud should contribute to the process input estimate value.

Referring again to FIG. 6, with the mean component of the process input estimate um(t), the difference component of the process input estimate at any time t, ud(t), and the difference gain at any time t, Kd(t), the scaled difference component uds(t) of the process input estimate may be determined (640) by multiplying the difference component of the process input estimate at any time t, ud(t) by the difference gain at any time t, Kd(t). Thereafter, the scaled difference component uds(t) of the process input estimate may be added to the mean component of the process input estimate um(t) to determine the current process input estimate value u(t) (650).

FIG. 7 is a flowchart illustrating the noise filtered estimation. Referring to FIG. 7, with an estimate of process input signal at any time t, u(t), and based on the measured signals from the analyte sensor z(t), in addition to past estimates of the noise-filtered signal xi(t−1), xi(t−2), . . . , the state observer 410 (FIG. 4) may be configured to determine the estimate of noise-filtered signal at any time t, xi(t). In one aspect, the state observer 410 (FIG. 4) may be configured to reduce the contribution of noise without introducing excessive undesirable distortion based on the estimate of process input signal at any time t, u(t), and the measured signals from the sensor z(t).

FIG. 8 is a flowchart illustrating signal dropout detection in accordance with one embodiment of the present invention. Referring to FIG. 8, a present “fast rate” estimate xdf(t) is determined based on present and past noise-filtered estimate of the signal (810). For example, a difference signal xd(t) may be determined based on the following expression:
xd(t)=xi(t)−xi(t−1)  (5)
Thereafter, a fast rate may be extracted from the difference signal xd(t) by performing high pass filtering on the difference signal xd(t). In one embodiment, a discrete-time realization of a first order high pass filter function may be used to determine the present “fast rate” estimate xdf(t):
xdf(t)=ahpfDxdf(t−1)+xd(t)−xd(t−1)  (6)

where the value of ahpfD, or the structure of the high pass filter may be determined in accordance with the suitable design configurations, for example, a value between zero and one.

Referring back to FIG. 8, after determining the “fast rate” estimate xdf(t), a present “slow rate” estimate xds(t) is determined based on present and past noise-filtered estimate of the signal (820). For example, in one embodiment, the slow rate estimate xds(t) may be determined by passing the simple difference through a low-pass filter, or alternatively, by taking the difference between the simple difference and the fast difference signals as shown, for example, by the following expression:
xds(t)=xd(t)−xdf(t)  (7)

After determining the slow rate estimate xds(t), it is determined whether there is a beginning of a large negative spike in the fast rate estimate xdf(t) (830). That is, referring to FIG. 8, the start of a signal dropout state is determined which is correlated to a spike in the fast difference. The fast difference does not generate a spike larger than a predetermined value in response to signals generated in the absence of dropouts. For example, adjusted to the units of glucose concentration, this may correspond to a fast rate in excess of −3 mg/(dL min). Although a rate of −3 mg/(dL min) or faster may be ascertained, when band pass filtered, the fast rate estimate xdf(t) determined above does not occur in this range unless a signal dropout occurs.

Referring back to FIG. 8, if the beginning of a large negative spike in the fast rate estimate xdf(t) is detected, then the elapsed time period from the initial occurrence of the large negative spike is monitored (840), for example, by triggering a timer or a counter so as to monitor the elapsed time since the most recent signal dropout occurrence predicted estimate. In this manner, a safety check mechanism may be provided to determine situations where a signal dropout that was anticipated to have started has lasted in an undesirably long period of dropout time period. That is, as the signal dropouts are generally intermittent in nature, it is expected that the dropout does not last beyond the order of one hour, for example, and more commonly, in the order of five to 30 minutes.

Thereafter, it is determined whether a predetermined allowable time period has elapsed (850). As shown in FIG. 8, if it is determined the allowable time period has not elapsed, then the beginning or onset of the signal dropout is estimated. On the other hand, if the predetermined allowable time period has elapsed, then the end of the signal dropout is estimated. Referring again to FIG. 8, when the beginning of a large negative spike in the fast rate estimate xdf(t) is not detected, it is determined whether an end of a large positive spike (for example, in the order of +3 mg/(dL min)) in the fast rate estimate xdf(t) is detected (860). If the end of the large positive spike in the fast rate estimate xdf(t) is detected, then the end of the signal dropout is estimated. On the other hand, if the end of the large positive spike in the fast rate estimate xdf(t) is not detected, then no signal dropout is estimated.

That is, a signal dropout is generally correlated to a large positive spike in the fast difference. Thus, in this case, the tail of the large positive spike is monitored and detected as the end of the signal dropout. In one embodiment, this maximizes the likelihood of detecting most of the instances within a signal dropout.

In this manner, in one embodiment of the present invention, the presence of signal dropout may be monitored and detected based on, for example, present and past noise filtered estimate of the signals.

FIG. 9 is a flowchart illustrating an overall signal dropout compensation in accordance with one embodiment of the present invention. Referring to FIG. 9, a momentum-based estimate is determined based on the present slow difference and previous momentum-based estimate (910). That is, with the present and past noise filtered estimate of the signal, the present and past slow and fast rate estimates determined as described above, and with the signal dropout detection estimation determined above, the momentum-based estimate is determined based on the present slow difference and previous momentum-based estimate. That is, in one embodiment, a momentum-based estimate may factor in a signal without dropouts as being likely to project (e.g., extrapolate) based on its past signal and its prior trend.

Referring back to FIG. 9, after determining the momentum based estimate using the present slow difference and prior momentum-based estimate, an averaged value of the present or current momentum-based estimate and the present noise filtered estimate is determined (920). Thereafter, an inertial gain based on the present and past slow rate estimate is determined (930), and which may be configured to scale the contribution of the momentum-based estimate determined using the present slow different and the previous momentum based estimate above in the final dropout compensated gain. Referring again to FIG. 9, after determining the inertial gain, a tracking gain is determined based on the inertial gain (940). In one embodiment, the determined tracking gain may be configured to scale the impact of the determined average value of the present momentum-based estimate and the present noise-filtered estimate, in the determination of the final dropout compensated signal (950) as discussed below.

Referring to FIG. 9, after determining the tracking gain, the dropout compensated signal is determined (950). In one embodiment, the dropout-compensated signal equals the noise-filtered estimate of the signal xi, when no dropout is estimated. Otherwise, the dropout compensated signal may be a weighted average of the momentum-based estimate (xmomentum) as discussed above and the averaged momentum and noise-filtered estimate (xaverage) also discussed above. In one aspect, the weighing factors for the weighted average of the momentum-based estimate (xmomentum) and the averaged momentum and noise-filtered estimate (xaverage) may be the inertial gain Kinertial and tracking gain Ktracking, respectively. For example, the dropout compensated signal at any time t, x′dci(t) in one embodiment may be determined in accordance with the following relationship:
x′dci(t)=(Kinertial(t)xmomentum(t))+(Ktracking(t)xaverage(t))  (8)

In a further embodiment, the determination of the dropout compensated signal at any time t, x′dci(t) may be refined to ensure a smooth transition depending upon the underlying conditions, as described in further detail below in conjunction with FIG. 10.

Referring back to FIG. 9, after determining the dropout compensated signal, the dropout compensated signal may be clipped to be within a predetermined range (960), for example, such that the dropout compensated signal is not less than the noise-filtered signal, and further, that it is not greater than a specified safety ratio times the noise-filtered signal.

In certain cases, the resulting value of the dropout compensated signal x′dci(t) may fall below the noise-filtered estimate xi(t). Since by definition, a dropout is a phenomena that can only reduce the true value of a signal, the relationship (8) above for determining the dropout compensated signal may be modified by ensuring that its value never goes below xi(t) at any given time, and as shown by the following expression:

x dci ⁡ ( t ) = { x dci ′ ⁡ ( t ) for ⁢ ⁢ x dci ′ ⁡ ( t ) ≥ ⁢ x i ⁡ ( t ) x i ⁡ ( t ) for ⁢ ⁢ x dci ′ ⁡ ( t ) < ⁢ x i ⁡ ( t ) ( 9 )

FIG. 10 is flowchart illustrating a detailed signal dropout compensation determination of FIG. 9 in accordance with one embodiment of the present invention. Referring to FIG. 10, for example, in determining the drop-compensated signal, it is first determined whether signal dropout is detected. If signal dropout is not detected, then it is determined whether a preset time period has elapsed since the end of the last dropout occurrence. If it is determined that a preset time period has elapsed, then the dropout compensated signal may be based upon the present noise filtered signal. In one aspect, the preset time period may be a predetermined time period that may be considered a long period of time. On the other hand, if it is determined that the preset time period has not elapsed (that is, the end of the occurrence of a signal dropout has recently occurred), then the dropout compensated signal may be based upon a smooth transition using the previous dropout compensated signal and the present noise filtered signal.

Indeed, referring to FIG. 10, it can be seen that depending upon the determination of the timing of the signal dropout occurrence, in particular embodiments, the dropout compensated signal may be determined based on one or more factors as shown in the Figure and also described above.

Referring again to the Figures, in particular embodiments, the processings associated with the noise filtering, signal dropout detection estimation and compensation may be performed by one or more processing units of the one or more receiver unit (104, 105) the transmitter unit 102 or the data processing terminal/infusion section 105. In addition, the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may also incorporate a blood glucose meter functionality, such that, the housing of the respective one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may include a test strip port configured to receive a blood sample for determining one or more blood glucose levels of the patient.

In a further embodiment, the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may be configured to receive the blood glucose value wirelessly over a communication link from, for example, a glucose meter. In still a further embodiment, the user or patient manipulating or using the analyte monitoring system 100 (FIG. 1) may manually input the blood glucose value using, for example, a user interface (for example, a keyboard, keypad, and the like) incorporated in the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105.

A method in one embodiment includes monitoring a data stream, generating a noise-filtered signal associated with the data stream, detecting a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.

In one aspect, generating the noise filtered signal may include generating one or more frequency-shaped signals based on the monitored data stream, and further, which may include high pass filtering the monitored data stream.

Also, generating the noise filtered signal in another aspect may be based on one or more previous noise filtered signals.

The method in a further embodiment may include outputting the noise filtered signal. The method in still another aspect may include outputting the noise filtered dropout compensated signal.

The method may also include generating a signal associated with detecting the presence of a signal dropout.

Moreover, the data stream in one embodiment may be associated with a monitored analyte levels of a patient.

An apparatus in another embodiment includes one or more processors, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.

The memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate one or more frequency-shaped signals based on the monitored data stream.

In another aspect, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the one or more frequency shaped signals by high pass filtering the monitored data stream.

In still another aspect, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the noise filtered signal based on one or more previous noise filtered signals.

Moreover, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered signal.

In yet another embodiment, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered dropout compensated signal.

Additionally, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate a signal associated with detecting the presence of a signal dropout.

A system in accordance with still another embodiment may include an analyte sensor configured to monitor an analyte of a patient, a data processing section operatively coupled to the analyte sensor, the data processing section further including one or more processors, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.

The data processing section may include a data transmission unit operatively coupled to one or more processors configured to transmit the data stream. In another aspect, the data processing section may include a data receiving unit operatively coupled to the one or more processors and configured to receive the data stream.

The analyte sensor may include a glucose sensor.

Moreover, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to store one or more of the data stream, the noise filtered signal, or the noise filtered dropout compensated signal.

The various processes described above including the processes performed by the receiver unit 104/105 or transmitter unit 102 in the software application execution environment in the analyte monitoring system 100 including the processes and routines described in conjunction with FIGS. 5-10, may be embodied as computer programs developed using an object oriented language that allows the modeling of complex systems with modular objects to create abstractions that are representative of real world, physical objects and their interrelationships. The software required to carry out the inventive process, which may be stored in the memory or storage unit of the receiver unit 104/105 or transmitter unit 102 may be developed by a person of ordinary skill in the art and may include one or more computer program products.

Various other modifications and alterations in the structure and method of operation of this invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. It is intended that the following claims define the scope of the present invention and that structures and methods within the scope of these claims and their equivalents be covered thereby.

Claims

1. An apparatus, comprising:

one or more processors; and
a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to monitor a data stream from an analyte sensor, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the detection of the presence of the signal dropout, wherein the generated noise filtered signal is based on one or more previous noise filtered signals.

2. The apparatus of claim 1 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to generate one or more frequency-shaped signals based on the monitored data stream.

3. The apparatus of claim 2 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to generate the one or more frequency shaped signals by high pass filtering the monitored data stream.

4. The apparatus of claim 1 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to output the noise filtered signal.

5. The apparatus of claim 1 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to output the noise filtered dropout compensated signal.

6. The apparatus of claim 1 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to generate a signal associated with detecting the presence of a signal dropout.

7. A system, comprising:

an analyte sensor configured to monitor an analyte of a patient; and
a data processing section operatively coupled to the analyte sensor, the data processing section further including: one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to monitor a data stream from the analyte sensor, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the detection of the presence of the signal dropout, wherein the generated noise filtered signal is based on one or more previous noise filtered signals.

8. The system of claim 7 wherein the data processing section includes a data transmission unit operatively coupled to the one or more processors configured to transmit the data stream.

9. The system of claim 7 wherein the data processing section includes a data receiving unit operatively coupled to the one or more processors and configured to receive the data stream.

10. The system of claim 7 wherein the analyte sensor includes a glucose sensor.

11. The system of claim 7 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to store one or more of the data stream, the noise filtered signal, or the noise filtered dropout compensated signal.

12. The apparatus of claim 1 wherein the monitored data stream is associated with a monitored analyte level of a patient.

13. The apparatus of claim 12 wherein the monitored analyte level includes a glucose level.

14. The system of claim 7 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to generate one or more frequency-shaped signals based on the monitored data stream.

15. The system of claim 14 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to generate the one or more frequency shaped signals by high pass filtering the monitored data stream.

16. The system of claim 7 further comprising an output unit operatively coupled to the data processing section.

17. The system of claim 16 wherein the output unit is configured to output the noise filtered signal.

18. The system of claim 16 wherein the output unit is configured to output the noise filtered dropout compensated signal.

19. The system of claim 16 wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to generate a signal associated with detecting the presence of a signal dropout, and further wherein the signal associated with detecting the presence of a signal dropout is output by the output unit.

Referenced Cited
U.S. Patent Documents
3581062 May 1971 Aston
3926760 December 1975 Allen et al.
3949388 April 6, 1976 Fuller
3978856 September 7, 1976 Michel
4036749 July 19, 1977 Anderson
4055175 October 25, 1977 Clemens et al.
4129128 December 12, 1978 McFarlane
4245634 January 20, 1981 Albisser et al.
4327725 May 4, 1982 Cortese et al.
4344438 August 17, 1982 Schultz
4349728 September 14, 1982 Phillips et al.
4373527 February 15, 1983 Fischell
4392849 July 12, 1983 Petre et al.
4425920 January 17, 1984 Bourland et al.
4462048 July 24, 1984 Ross
4478976 October 23, 1984 Goertz et al.
4494950 January 22, 1985 Fischell
4509531 April 9, 1985 Ward
4527240 July 2, 1985 Kvitash
4538616 September 3, 1985 Rogoff
4619793 October 28, 1986 Lee
4671288 June 9, 1987 Gough
4703756 November 3, 1987 Gough et al.
4731726 March 15, 1988 Allen, III
4749985 June 7, 1988 Corsberg
4757022 July 12, 1988 Shults et al.
4777953 October 18, 1988 Ash et al.
4779618 October 25, 1988 Mund et al.
4854322 August 8, 1989 Ash et al.
4871351 October 3, 1989 Feingold
4890620 January 2, 1990 Gough
4925268 May 15, 1990 Iyer et al.
4953552 September 4, 1990 DeMarzo
4986271 January 22, 1991 Wilkins
4995402 February 26, 1991 Smith et al.
5000180 March 19, 1991 Kuypers et al.
5002054 March 26, 1991 Ash et al.
5019974 May 28, 1991 Beckers
5050612 September 24, 1991 Matsumura
5055171 October 8, 1991 Peck
5068536 November 26, 1991 Rosenthal
5082550 January 21, 1992 Rishpon et al.
5106365 April 21, 1992 Hernandez
5122925 June 16, 1992 Inpyn
5165407 November 24, 1992 Wilson et al.
5231988 August 3, 1993 Wernicke et al.
5246867 September 21, 1993 Lakowicz et al.
5262035 November 16, 1993 Gregg et al.
5262305 November 16, 1993 Heller et al.
5264104 November 23, 1993 Gregg et al.
5264105 November 23, 1993 Gregg et al.
5279294 January 18, 1994 Anderson et al.
5285792 February 15, 1994 Sjoquist et al.
5293877 March 15, 1994 O'Hara et al.
5299571 April 5, 1994 Mastrototaro
5320725 June 14, 1994 Gregg et al.
5322063 June 21, 1994 Allen et al.
5340722 August 23, 1994 Wolfbeis et al.
5342789 August 30, 1994 Chick et al.
5356786 October 18, 1994 Heller et al.
5360404 November 1, 1994 Novacek et al.
5372427 December 13, 1994 Padovani et al.
5376070 December 27, 1994 Purvis et al.
5379238 January 3, 1995 Stark
5384547 January 24, 1995 Lynk et al.
5390671 February 21, 1995 Lord et al.
5391250 February 21, 1995 Cheney, II et al.
5408999 April 25, 1995 Singh et al.
5411647 May 2, 1995 Johnson et al.
5425868 June 20, 1995 Pedersen
5431160 July 11, 1995 Wilkins
5431921 July 11, 1995 Thombre
5462645 October 31, 1995 Albery et al.
5497772 March 12, 1996 Schulman et al.
5507288 April 16, 1996 Bocker et al.
5509410 April 23, 1996 Hill et al.
5514718 May 7, 1996 Lewis et al.
5531878 July 2, 1996 Vadgama et al.
5568400 October 22, 1996 Stark et al.
5568806 October 29, 1996 Cheney, II et al.
5569186 October 29, 1996 Lord et al.
5582184 December 10, 1996 Erickson et al.
5586553 December 24, 1996 Halili et al.
5593852 January 14, 1997 Heller et al.
5609575 March 11, 1997 Larson et al.
5628310 May 13, 1997 Rao et al.
5653239 August 5, 1997 Pompei et al.
5660163 August 26, 1997 Schulman et al.
5665222 September 9, 1997 Heller et al.
5711001 January 20, 1998 Bussan et al.
5711861 January 27, 1998 Ward et al.
5733259 March 31, 1998 Valcke et al.
5772586 June 30, 1998 Heinonen et al.
5791344 August 11, 1998 Schulman et al.
5899855 May 4, 1999 Brown
5925021 July 20, 1999 Castellano et al.
5935224 August 10, 1999 Svancarek et al.
5942979 August 24, 1999 Luppino
5957854 September 28, 1999 Besson et al.
5964993 October 12, 1999 Blubaugh, Jr. et al.
5965380 October 12, 1999 Heller et al.
5971922 October 26, 1999 Arita et al.
5995860 November 30, 1999 Sun et al.
6001067 December 14, 1999 Shults et al.
6024699 February 15, 2000 Surwit et al.
6049727 April 11, 2000 Crothall
6073031 June 6, 2000 Helstab et al.
6083710 July 4, 2000 Heller et al.
6088608 July 11, 2000 Schulman et al.
6091976 July 18, 2000 Pfeiffer et al.
6093172 July 25, 2000 Funderburk et al.
6103033 August 15, 2000 Say et al.
6117290 September 12, 2000 Say et al.
6119028 September 12, 2000 Schulman et al.
6120676 September 19, 2000 Heller et al.
6121009 September 19, 2000 Heller et al.
6121611 September 19, 2000 Lindsay et al.
6122351 September 19, 2000 Schlueter, Jr. et al.
6134461 October 17, 2000 Say et al.
6162611 December 19, 2000 Heller et al.
6175752 January 16, 2001 Say et al.
6200265 March 13, 2001 Walsh et al.
6212416 April 3, 2001 Ward et al.
6219574 April 17, 2001 Cormier et al.
6233471 May 15, 2001 Berner et al.
6248067 June 19, 2001 Causey, III et al.
6275717 August 14, 2001 Gross et al.
6284478 September 4, 2001 Heller et al.
6293925 September 25, 2001 Safabash et al.
6295506 September 25, 2001 Heinonen et al.
6306104 October 23, 2001 Cunningham et al.
6309884 October 30, 2001 Cooper et al.
6329161 December 11, 2001 Heller et al.
6360888 March 26, 2002 McIvor et al.
6366794 April 2, 2002 Moussy et al.
6377828 April 23, 2002 Chaiken et al.
6379301 April 30, 2002 Worthington et al.
6424847 July 23, 2002 Mastrototaro et al.
6427088 July 30, 2002 Bowman, IV et al.
6440068 August 27, 2002 Brown et al.
6478736 November 12, 2002 Mault
6484046 November 19, 2002 Say et al.
6514718 February 4, 2003 Heller et al.
6544212 April 8, 2003 Galley et al.
6551494 April 22, 2003 Heller et al.
6558320 May 6, 2003 Causey, III et al.
6558321 May 6, 2003 Burd et al.
6558351 May 6, 2003 Steil et al.
6560471 May 6, 2003 Heller et al.
6561978 May 13, 2003 Conn et al.
6562001 May 13, 2003 Lebel et al.
6564105 May 13, 2003 Starkweather et al.
6565509 May 20, 2003 Say et al.
6571128 May 27, 2003 Lebel et al.
6576101 June 10, 2003 Heller et al.
6577899 June 10, 2003 Lebel et al.
6579690 June 17, 2003 Bonnecaze et al.
6585644 July 1, 2003 Lebel et al.
6591125 July 8, 2003 Buse et al.
6595919 July 22, 2003 Berner et al.
6605200 August 12, 2003 Mao et al.
6605201 August 12, 2003 Mao et al.
6607509 August 19, 2003 Bobroff et al.
6610012 August 26, 2003 Mault
6633772 October 14, 2003 Ford et al.
6635014 October 21, 2003 Starkweather et al.
6641533 November 4, 2003 Causey, III et al.
6648821 November 18, 2003 Lebel et al.
6654625 November 25, 2003 Say et al.
6659948 December 9, 2003 Lebel et al.
6668196 December 23, 2003 Villegas et al.
6687546 February 3, 2004 Lebel et al.
6689056 February 10, 2004 Kilcoyne et al.
6694191 February 17, 2004 Starkweather et al.
6695860 February 24, 2004 Ward et al.
6698269 March 2, 2004 Baber et al.
6702857 March 9, 2004 Brauker et al.
6733446 May 11, 2004 Lebel et al.
6740075 May 25, 2004 Lebel et al.
6741877 May 25, 2004 Shults et al.
6746582 June 8, 2004 Heller et al.
6758810 July 6, 2004 Lebel et al.
6770030 August 3, 2004 Schaupp et al.
6790178 September 14, 2004 Mault et al.
6809653 October 26, 2004 Mann et al.
6810290 October 26, 2004 Lebel et al.
6811533 November 2, 2004 Lebel et al.
6811534 November 2, 2004 Bowman, IV et al.
6813519 November 2, 2004 Lebel et al.
6862465 March 1, 2005 Shults et al.
6873268 March 29, 2005 Lebel et al.
6881551 April 19, 2005 Heller et al.
6892085 May 10, 2005 McIvor et al.
6895263 May 17, 2005 Shin et al.
6895265 May 17, 2005 Silver
6931327 August 16, 2005 Goode, Jr. et al.
6932894 August 23, 2005 Mao et al.
6936006 August 30, 2005 Sabra
6950708 September 27, 2005 Bowman, IV et al.
6958705 October 25, 2005 Lebel et al.
6968294 November 22, 2005 Gutta et al.
6971274 December 6, 2005 Olin
6974437 December 13, 2005 Lebel et al.
6990366 January 24, 2006 Say et al.
6997907 February 14, 2006 Safabash et al.
6998247 February 14, 2006 Monfre et al.
7003336 February 21, 2006 Holker et al.
7003340 February 21, 2006 Say et al.
7003341 February 21, 2006 Say et al.
7022072 April 4, 2006 Fox et al.
7024245 April 4, 2006 Lebel et al.
7029444 April 18, 2006 Shin et al.
7041068 May 9, 2006 Freeman et al.
7052483 May 30, 2006 Wojcik
7056302 June 6, 2006 Douglas
7074307 July 11, 2006 Simpson et al.
7081195 July 25, 2006 Simpson et al.
7098803 August 29, 2006 Mann et al.
7108778 September 19, 2006 Simpson et al.
7110803 September 19, 2006 Shults et al.
7113821 September 26, 2006 Sun et al.
7134999 November 14, 2006 Brauker et al.
7136689 November 14, 2006 Shults et al.
7171274 January 30, 2007 Starkweather et al.
7190988 March 13, 2007 Say et al.
7192450 March 20, 2007 Brauker et al.
7198606 April 3, 2007 Boecker et al.
7226978 June 5, 2007 Tapsak et al.
7267665 September 11, 2007 Steil et al.
7276029 October 2, 2007 Goode, Jr. et al.
7299082 November 20, 2007 Feldman et al.
7310544 December 18, 2007 Brister et al.
7317938 January 8, 2008 Lorenz et al.
7335294 February 26, 2008 Heller et al.
7354420 April 8, 2008 Steil et al.
7364592 April 29, 2008 Carr-Brendel et al.
7366556 April 29, 2008 Brister et al.
7379765 May 27, 2008 Petisce et al.
7402153 July 22, 2008 Steil et al.
7424318 September 9, 2008 Brister et al.
7460898 December 2, 2008 Brister et al.
7467003 December 16, 2008 Brister et al.
7471972 December 30, 2008 Rhodes et al.
7494465 February 24, 2009 Brister et al.
7497827 March 3, 2009 Brister et al.
7499002 March 3, 2009 Blasko et al.
7519408 April 14, 2009 Rasdal et al.
7547281 June 16, 2009 Hayes et al.
7569030 August 4, 2009 Lebel et al.
7583990 September 1, 2009 Goode, Jr. et al.
7591801 September 22, 2009 Brauker et al.
7599726 October 6, 2009 Goode, Jr. et al.
7613491 November 3, 2009 Boock et al.
7615007 November 10, 2009 Shults et al.
7618369 November 17, 2009 Hayter et al.
7632228 December 15, 2009 Brauker et al.
20010041831 November 15, 2001 Starkweather et al.
20020019022 February 14, 2002 Dunn et al.
20020042090 April 11, 2002 Heller et al.
20020103499 August 1, 2002 Perez et al.
20020106709 August 8, 2002 Potts et al.
20020128594 September 12, 2002 Das et al.
20020161288 October 31, 2002 Shin et al.
20020169635 November 14, 2002 Shillingburg
20020193679 December 19, 2002 Malave et al.
20030004403 January 2, 2003 Drinan et al.
20030023317 January 30, 2003 Brauker et al.
20030032874 February 13, 2003 Rhodes et al.
20030042137 March 6, 2003 Mao et al.
20030050546 March 13, 2003 Desai et al.
20030065308 April 3, 2003 Lebel et al.
20030100821 May 29, 2003 Heller et al.
20030125612 July 3, 2003 Fox et al.
20030130616 July 10, 2003 Steil et al.
20030134347 July 17, 2003 Heller et al.
20030168338 September 11, 2003 Gao et al.
20030176933 September 18, 2003 Lebel et al.
20030187338 October 2, 2003 Say et al.
20030199790 October 23, 2003 Boecker et al.
20030208113 November 6, 2003 Mault et al.
20030212317 November 13, 2003 Kovatchev et al.
20030212379 November 13, 2003 Bylund et al.
20030216630 November 20, 2003 Jersey-Willuhn et al.
20030217966 November 27, 2003 Tapsak et al.
20040010207 January 15, 2004 Flaherty et al.
20040011671 January 22, 2004 Shults et al.
20040039298 February 26, 2004 Abreu
20040040840 March 4, 2004 Mao et al.
20040045879 March 11, 2004 Shults et al.
20040064068 April 1, 2004 DeNuzzio et al.
20040106858 June 3, 2004 Say et al.
20040122353 June 24, 2004 Shahmirian et al.
20040133164 July 8, 2004 Funderburk et al.
20040138588 July 15, 2004 Saikley et al.
20040146909 July 29, 2004 Duong et al.
20040152622 August 5, 2004 Keith et al.
20040167801 August 26, 2004 Say et al.
20040171921 September 2, 2004 Say et al.
20040176672 September 9, 2004 Silver et al.
20040186362 September 23, 2004 Brauker et al.
20040186365 September 23, 2004 Jin et al.
20040193025 September 30, 2004 Steil et al.
20040193090 September 30, 2004 Lebel et al.
20040197846 October 7, 2004 Hockersmith et al.
20040199059 October 7, 2004 Brauker et al.
20040204687 October 14, 2004 Mogensen et al.
20040208780 October 21, 2004 Faries, Jr. et al.
20040225338 November 11, 2004 Lebel et al.
20040236200 November 25, 2004 Say et al.
20040254433 December 16, 2004 Bandis et al.
20040263354 December 30, 2004 Mann et al.
20040267300 December 30, 2004 Mace
20050003470 January 6, 2005 Nelson et al.
20050004439 January 6, 2005 Shin et al.
20050004494 January 6, 2005 Perez et al.
20050010087 January 13, 2005 Banet et al.
20050010269 January 13, 2005 Lebel et al.
20050016276 January 27, 2005 Guan et al.
20050027177 February 3, 2005 Shin et al.
20050031689 February 10, 2005 Shults et al.
20050038332 February 17, 2005 Saidara et al.
20050043598 February 24, 2005 Goode, Jr. et al.
20050090607 April 28, 2005 Tapsak et al.
20050096511 May 5, 2005 Fox et al.
20050096512 May 5, 2005 Fox et al.
20050112169 May 26, 2005 Brauker et al.
20050113653 May 26, 2005 Fox et al.
20050114068 May 26, 2005 Chey et al.
20050121322 June 9, 2005 Say et al.
20050131346 June 16, 2005 Douglas
20050143635 June 30, 2005 Kamath et al.
20050176136 August 11, 2005 Burd et al.
20050177398 August 11, 2005 Watanabe et al.
20050182306 August 18, 2005 Sloan
20050187720 August 25, 2005 Goode, Jr. et al.
20050192494 September 1, 2005 Ginsberg
20050192557 September 1, 2005 Brauker et al.
20050195930 September 8, 2005 Spital et al.
20050199494 September 15, 2005 Say et al.
20050203360 September 15, 2005 Brauker et al.
20050239154 October 27, 2005 Feldman et al.
20050241957 November 3, 2005 Mao et al.
20050245795 November 3, 2005 Goode, Jr. et al.
20050245799 November 3, 2005 Brauker et al.
20050245839 November 3, 2005 Stivoric et al.
20050245904 November 3, 2005 Estes et al.
20050287620 December 29, 2005 Heller et al.
20060001538 January 5, 2006 Kraft et al.
20060004270 January 5, 2006 Bedard et al.
20060015020 January 19, 2006 Neale et al.
20060015024 January 19, 2006 Brister et al.
20060016700 January 26, 2006 Brister et al.
20060019327 January 26, 2006 Brister et al.
20060020186 January 26, 2006 Brister et al.
20060020187 January 26, 2006 Brister et al.
20060020188 January 26, 2006 Kamath et al.
20060020189 January 26, 2006 Brister et al.
20060020190 January 26, 2006 Kamath et al.
20060020191 January 26, 2006 Brister et al.
20060020192 January 26, 2006 Brister et al.
20060029177 February 9, 2006 Cranford, Jr. et al.
20060031094 February 9, 2006 Cohen et al.
20060036139 February 16, 2006 Brister et al.
20060036140 February 16, 2006 Brister et al.
20060036141 February 16, 2006 Kamath et al.
20060036142 February 16, 2006 Brister et al.
20060036143 February 16, 2006 Brister et al.
20060036144 February 16, 2006 Brister et al.
20060036145 February 16, 2006 Brister et al.
20060155180 July 13, 2006 Brister et al.
20060166629 July 27, 2006 Reggiardo
20060173260 August 3, 2006 Gaoni et al.
20060173406 August 3, 2006 Hayes et al.
20060173444 August 3, 2006 Choy et al.
20060183985 August 17, 2006 Brister et al.
20060189863 August 24, 2006 Peyser et al.
20060222566 October 5, 2006 Brauker et al.
20060224109 October 5, 2006 Steil et al.
20060226985 October 12, 2006 Goodnow et al.
20060229512 October 12, 2006 Petisce et al.
20060247508 November 2, 2006 Fennell
20060272652 December 7, 2006 Stocker et al.
20060281985 December 14, 2006 Ward et al.
20070016381 January 18, 2007 Kamath et al.
20070027381 February 1, 2007 Stafford
20070032706 February 8, 2007 Kamath et al.
20070033074 February 8, 2007 Nitzan et al.
20070060803 March 15, 2007 Liljeryd et al.
20070060814 March 15, 2007 Stafford
20070066873 March 22, 2007 Kamath et al.
20070071681 March 29, 2007 Gadkar et al.
20070073129 March 29, 2007 Shah et al.
20070078320 April 5, 2007 Stafford
20070078321 April 5, 2007 Mazza et al.
20070078322 April 5, 2007 Stafford
20070078323 April 5, 2007 Reggiardo et al.
20070106135 May 10, 2007 Sloan et al.
20070118405 May 24, 2007 Campbell et al.
20070124002 May 31, 2007 Estes et al.
20070149875 June 28, 2007 Ouyang et al.
20070163880 July 19, 2007 Woo et al.
20070168224 July 19, 2007 Letzt et al.
20070173706 July 26, 2007 Neinast et al.
20070173761 July 26, 2007 Kanderian et al.
20070179349 August 2, 2007 Hoyme et al.
20070179352 August 2, 2007 Randlov et al.
20070191701 August 16, 2007 Feldman et al.
20070203407 August 30, 2007 Hoss et al.
20070203966 August 30, 2007 Brauker et al.
20070213657 September 13, 2007 Jennewine et al.
20070232877 October 4, 2007 He
20070235331 October 11, 2007 Simpson et al.
20070249922 October 25, 2007 Peyser et al.
20080009692 January 10, 2008 Stafford
20080017522 January 24, 2008 Heller et al.
20080021666 January 24, 2008 Goode, Jr. et al.
20080029391 February 7, 2008 Mao et al.
20080033254 February 7, 2008 Kamath et al.
20080039702 February 14, 2008 Hayter et al.
20080045824 February 21, 2008 Tapsak et al.
20080058625 March 6, 2008 McGarraugh et al.
20080064937 March 13, 2008 McGarraugh et al.
20080071156 March 20, 2008 Brister et al.
20080071157 March 20, 2008 McGarraugh et al.
20080071158 March 20, 2008 McGarraugh et al.
20080081977 April 3, 2008 Hayter et al.
20080083617 April 10, 2008 Simpson et al.
20080086042 April 10, 2008 Brister et al.
20080086044 April 10, 2008 Brister et al.
20080086273 April 10, 2008 Shults et al.
20080097289 April 24, 2008 Steil et al.
20080108942 May 8, 2008 Brister et al.
20080154513 June 26, 2008 Kovatchev et al.
20080167543 July 10, 2008 Say et al.
20080172205 July 17, 2008 Breton et al.
20080183060 July 31, 2008 Steil et al.
20080183061 July 31, 2008 Goode et al.
20080183399 July 31, 2008 Goode et al.
20080188731 August 7, 2008 Brister et al.
20080188796 August 7, 2008 Steil et al.
20080189051 August 7, 2008 Goode et al.
20080194935 August 14, 2008 Brister et al.
20080194936 August 14, 2008 Goode et al.
20080194937 August 14, 2008 Goode et al.
20080194938 August 14, 2008 Brister et al.
20080195232 August 14, 2008 Carr-Brendel et al.
20080195967 August 14, 2008 Goode et al.
20080197024 August 21, 2008 Simpson et al.
20080200788 August 21, 2008 Brister et al.
20080200789 August 21, 2008 Brister et al.
20080200791 August 21, 2008 Simpson et al.
20080208025 August 28, 2008 Shults et al.
20080208113 August 28, 2008 Damiano et al.
20080214915 September 4, 2008 Brister et al.
20080214918 September 4, 2008 Brister et al.
20080228051 September 18, 2008 Shults et al.
20080228054 September 18, 2008 Shults et al.
20080242961 October 2, 2008 Brister et al.
20080255434 October 16, 2008 Hayter et al.
20080255437 October 16, 2008 Hayter
20080255808 October 16, 2008 Hayter
20080256048 October 16, 2008 Hayter
20080262469 October 23, 2008 Brister et al.
20080275313 November 6, 2008 Brister et al.
20080287761 November 20, 2008 Hayter
20080287762 November 20, 2008 Hayter
20080287763 November 20, 2008 Hayter
20080287764 November 20, 2008 Rasdal et al.
20080287765 November 20, 2008 Rasdal et al.
20080287766 November 20, 2008 Rasdal et al.
20080288180 November 20, 2008 Hayter
20080288204 November 20, 2008 Hayter et al.
20080296155 December 4, 2008 Shults et al.
20080306368 December 11, 2008 Goode et al.
20080306434 December 11, 2008 Dobbles et al.
20080306435 December 11, 2008 Kamath et al.
20080306444 December 11, 2008 Brister et al.
20080312841 December 18, 2008 Hayter
20080312842 December 18, 2008 Hayter
20080312844 December 18, 2008 Hayter et al.
20080312845 December 18, 2008 Hayter et al.
20080319279 December 25, 2008 Ramsay et al.
20090005665 January 1, 2009 Hayter et al.
20090006034 January 1, 2009 Hayter et al.
20090012379 January 8, 2009 Goode et al.
20090018424 January 15, 2009 Kamath et al.
20090030294 January 29, 2009 Petisce et al.
20090033482 February 5, 2009 Hayter et al.
20090036747 February 5, 2009 Hayter et al.
20090036758 February 5, 2009 Brauker et al.
20090036760 February 5, 2009 Hayter
20090036763 February 5, 2009 Brauker et al.
20090043181 February 12, 2009 Brauker et al.
20090043182 February 12, 2009 Brauker et al.
20090043525 February 12, 2009 Brauker et al.
20090043541 February 12, 2009 Brauker et al.
20090043542 February 12, 2009 Brauker et al.
20090045055 February 19, 2009 Rhodes et al.
20090055149 February 26, 2009 Hayter et al.
20090062633 March 5, 2009 Brauker et al.
20090062635 March 5, 2009 Brauker et al.
20090062767 March 5, 2009 VanAntwerp et al.
20090063402 March 5, 2009 Hayter
20090076356 March 19, 2009 Simpson et al.
20090076360 March 19, 2009 Brister et al.
20090076361 March 19, 2009 Kamath et al.
20090099436 April 16, 2009 Brister et al.
20090105636 April 23, 2009 Hayter et al.
20090124877 May 14, 2009 Goode, Jr. et al.
20090124878 May 14, 2009 Goode et al.
20090124879 May 14, 2009 Brister et al.
20090124964 May 14, 2009 Leach et al.
20090131768 May 21, 2009 Simpson et al.
20090131769 May 21, 2009 Leach et al.
20090131776 May 21, 2009 Simpson et al.
20090131777 May 21, 2009 Simpson et al.
20090137886 May 28, 2009 Shariati et al.
20090137887 May 28, 2009 Shariati et al.
20090143659 June 4, 2009 Li et al.
20090143660 June 4, 2009 Brister et al.
20090156919 June 18, 2009 Brister et al.
20090156924 June 18, 2009 Shariati et al.
20090163790 June 25, 2009 Brister et al.
20090163791 June 25, 2009 Brister et al.
20090164190 June 25, 2009 Hayter
20090164239 June 25, 2009 Hayter et al.
20090164251 June 25, 2009 Hayter
20090178459 July 16, 2009 Li et al.
20090182217 July 16, 2009 Li et al.
20090192366 July 30, 2009 Mensinger et al.
20090192380 July 30, 2009 Shariati et al.
20090192722 July 30, 2009 Shariati et al.
20090192724 July 30, 2009 Brauker et al.
20090192745 July 30, 2009 Kamath et al.
20090192751 July 30, 2009 Kamath et al.
20090198118 August 6, 2009 Hayter et al.
20090203981 August 13, 2009 Brauker et al.
20090204341 August 13, 2009 Brauker et al.
20090216103 August 27, 2009 Brister et al.
20090240120 September 24, 2009 Mensinger et al.
20090240128 September 24, 2009 Mensinger et al.
20090240193 September 24, 2009 Mensinger et al.
20090242399 October 1, 2009 Kamath et al.
20090242425 October 1, 2009 Kamath et al.
20090247855 October 1, 2009 Boock et al.
20090247856 October 1, 2009 Boock et al.
20090247857 October 1, 2009 Harper et al.
20090287073 November 19, 2009 Boock et al.
20090287074 November 19, 2009 Shults et al.
20090299155 December 3, 2009 Yang et al.
20090299156 December 3, 2009 Simpson et al.
20090299162 December 3, 2009 Brauker et al.
20090299276 December 3, 2009 Brauker et al.
20100057040 March 4, 2010 Hayter
20100057041 March 4, 2010 Hayter
20100057042 March 4, 2010 Hayter
20100057044 March 4, 2010 Hayter
20100057057 March 4, 2010 Hayter et al.
Foreign Patent Documents
2667930 April 2011 CA
4401400 July 1995 DE
0098592 January 1984 EP
0127958 December 1984 EP
0320109 June 1989 EP
0353328 February 1990 EP
0390390 October 1990 EP
0396788 November 1990 EP
0286118 January 1995 EP
1048264 November 2000 EP
WO-96/25089 August 1996 WO
WO-96/35370 November 1996 WO
WO-98/35053 August 1998 WO
WO-99/56613 November 1999 WO
WO-00/49940 August 2000 WO
WO-00/59370 October 2000 WO
WO-00/74753 December 2000 WO
WO-00/78992 December 2000 WO
WO-01/52935 July 2001 WO
WO-01/54753 August 2001 WO
WO-02/16905 February 2002 WO
WO-02/058537 August 2002 WO
WO-03/076893 September 2003 WO
WO-03/082091 October 2003 WO
WO-03/085372 October 2003 WO
WO-2004/060455 July 2004 WO
WO-2004/061420 July 2004 WO
WO-2005/041766 May 2005 WO
WO-2005/065542 July 2005 WO
WO-2005/089103 September 2005 WO
WO-2006/024671 March 2006 WO
WO-2006/079114 July 2006 WO
WO-2006/086423 August 2006 WO
WO-2006/118947 November 2006 WO
WO-2006/124099 November 2006 WO
WO-2007/016399 February 2007 WO
WO-2007/027788 March 2007 WO
WO-2007/041069 April 2007 WO
WO-2007/041070 April 2007 WO
WO-2007/041072 April 2007 WO
WO-2007/041248 April 2007 WO
WO-2007/056638 May 2007 WO
WO-2007/101223 September 2007 WO
WO-2007/115094 October 2007 WO
WO-2007/120363 October 2007 WO
WO-2007/126444 November 2007 WO
WO-2007/053832 December 2007 WO
WO-2007/143225 December 2007 WO
WO-2008/021913 February 2008 WO
WO-2008/042760 April 2008 WO
WO-2008/052057 May 2008 WO
WO-2008/128210 October 2008 WO
WO-2008/130896 October 2008 WO
WO-2008/130897 October 2008 WO
WO-2008/130898 October 2008 WO
WO-2008/143943 November 2008 WO
WO-2009/018058 February 2009 WO
WO-2009/086216 July 2009 WO
WO-2009/096992 August 2009 WO
WO-2009/097594 August 2009 WO
Other references
  • Armour, J. C., et al., “Application of Chronic Intravascular Blood Glucose Sensor in Dogs”, Diabetes, vol. 39, 1990, pp. 1519-1526.
  • Bennion, N., et al., “Alternate Site Glucose Testing: A Crossover Design”, Diabetes Technology & Therapeutics, vol. 4, No. 1, 2002, pp. 25-33.
  • Blank, T. B., et al., “Clinical Results From a Non-Invasive Blood Glucose Monitor”, Optical Diagnostics and Sensing of Biological Fluids and Glucose and Cholesterol Monitoring II, Proceedings of SPIE, vol. 4624, 2002, pp. 1-10.
  • Brooks, S. L., et al., “Development of an On-Line Glucose Sensor for Fermentation Monitoring”, Biosensors, vol. 3, 1987/88, pp. 45-56.
  • Cass, A. E., et al., “Ferrocene-Medicated Enzyme Electrode for Amperometric Determination of Glucose”, Analytical Chemistry, vol. 56, No. 4, 1984, 667-671.
  • Csoregi, E., et al., “Design and Optimization of a Selective Subcutaneously Implantable Glucose Electrode Based on ‘Wired’ Glucose Oxidase”, Analytical Chemistry, vol. 67, No. 7, 1995, pp. 1240-1244.
  • Feldman, B., et al., “A Continuous Glucose Sensor Based on Wired Enzyme™ Technology—Results from a 3-Day Trial in Patients with Type 1 Diabetes”, Diabetes Technology & Therapeutics, vol. 5, No. 5, 2003, pp. 769-779.
  • Feldman, B., et al., “Correlation of Glucose Concentrations in Interstitial Fluid and Venous Blood During Periods of Rapid Glucose Change”, Abbott Diabetes Care, Inc. Freestyle Navigator Contiuous Glucose Monitor Pamphlet, 2004.
  • Isermann, R., “Supervision, Fault-Detection and Fault-Diagnosis Methods—An Introduction”, Control Engineering Practice, vol. 5, No. 5, 1997, pp. 639-652.
  • Isermann, R., et al., “Trends in the Application of Model-Based Fault Detection and Diagnosis of Technical Processes”, Control Engineering Practice, vol. 5, No. 5, 1997, pp. 709-719.
  • Johnson, P. C., “Peripheral Circulation”, John Wiley & Sons, 1978, pp. 198.
  • Jungheim, K., et al., “How Rapid Does Glucose Concentration Change in Daily Life of Patients with Type 1 Diabetes?”, 2002, pp. 250.
  • Jungheim, K., et al., “Risky Delay of Hypoglycemia Detection by Glucose Monitoring at the Arm”, Diabetes Care, vol. 24, No. 7, 2001, pp. 1303-1304.
  • Kaplan, S. M., “Wiley Electrical and Electronics Engineering Dictionary”, IEEE Press, 2004, pp. 141, 142, 548, 549.
  • Lortz, J., et al., “What is Bluetooth? We Explain the Newest Short-Range Connectivity Technology”, Smart Computing Learning Series, Wireless Computing, vol. 8, Issue 5, 2002, pp. 72-74.
  • Malin, S. F., et al., “Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectoscopy”, Clinical Chemistry, vol. 45, No. 9, 1999, pp. 1651-1658.
  • McGarraugh, G., et al., “Glucose Measurements Using Blood Extracted from the Forearm and the Finger”, TheraSense, Inc., 2001, 16 Pages.
  • McGarraugh, G., et al., “Physiological Influences on Off-Finger Glucose Testing”, Diabetes Technology & Therapeutics, vol. 3, No. 3, 2001, pp. 367-376.
  • McKean, B. D., et al., “A Telemetry-Instrumentation System for Chronically Implanted Glucose and Oxygen Sensors”, IEEE Transactions on Biomedical Engineering, vol. 35, No. 7, 1988, pp. 526-532.
  • Pickup, J., et al., “Implantable Glucose Sensors: Choosing the Appropriate Sensing Strategy”, Biosensors, vol. 3, 1987/88, pp. 335-346.
  • Pickup, J., et al., “In Vivo Molecular Sensing in Diabetes Mellitus: An Implantable Glucose Sensor with Direct Electron Transfer”, Diabetologia, vol. 32, 1989, pp. 213-217.
  • Pishko, M. V., et al., “Amperometric Glucose Microelectrodes Prepared Through Immobilization of Glucose Oxidase in Redox Hydrogels”, Analytical Chemistry, vol. 63, No. 20, 1991, pp. 2268-2272.
  • Quinn, C. P., et al., “Kinetics of Glucose Delivery to Subcutaneous Tissue in Rats Measured with 0.3-mm Amperometric Microsensors”, The American Physiological Society, 1995, E155-E161.
  • Roe, J. N., et al., “Bloodless Glucose Measurements”, Critical Review in Therapeutic Drug Carrier Systems, vol. 15, Issue 3, 1998, pp. 199-241.
  • Sakakida, M., et al., “Development of Ferrocene-Mediated Needle-Type Glucose Sensor as a Measure of True Subcutaneous Tissue Glucose Concentrations”, Artificial Organs Today, vol. 2, No. 2, 1992, pp. 145-158.
  • Sakakida, M., et al., “Ferrocene-Mediated Needle-Type Glucose Sensor Covered with Newly Designed Biocompatible Membrane”, Sensors and Actuators B, vol. 13-14, 1993, pp. 319-322.
  • Salehi, C., et al., “A Telemetry-Instrumentation System for Long-Term Implantable Glucose and Oxygen Sensors”, Analytical Letters, vol. 29, No. 13, 1996, pp. 2289-2308.
  • Schmidtke, D. W., et al., “Measurement and Modeling of the Transient Difference Between Blood and Subcutaneous Glucose Concentrations in the Rat After Injection of Insulin”, Proceedings of the National Academy of Sciences, vol. 95, 1998, pp. 294-299.
  • Shaw, G. W., et al., “In Vitro Testing of a Simply Constructed, Highly Stable Glucose Sensor Suitable for Implantation in Diabetic Patients”, Biosensors & Bioelectronics, vol. 6, 1991, pp. 401-406.
  • Shichiri, M., et al., “Glycaemic Control in Pancreatectomized Dogs with a Wearable Artificial Endocrine Pancreas”, Diabetologia, vol. 24, 1983, pp. 179-184.
  • Shichiri, M., et al., “In Vivo Characteristics of Needle-Type Glucose Sensor—Measurements of Subcutaneous Glucose Concentrations in Human Volunteers”, Hormone and Metabolic Research Supplement Series, vol. 20, 1988, pp. 17-20.
  • Shichiri, M., et al., “Membrane Design for Extending the Long-Life of an Implantable Glucose Sensor”, Diabetes Nutrition and Metabolism, vol. 2, 1989, pp. 309-313.
  • Shichiri, M., et al., “Needle-type Glucose Sensor for Wearable Artificial Endocrine Pancreas”, Implantable Sensors for Closed-Loop Prosthetic Systems, Chapter 15, 1985, pp. 197-210.
  • Shichiri, M., et al., “Telemetry Glucose Monitoring Device With Needle-Type Glucose Sensor: A Useful Tool for Blood Glucose Monitoring in Diabetic Individuals”, Diabetes Care, vol. 9, No. 3, 1986, pp. 298-301.
  • Shichiri, M., et al., “Wearable Artificial Endocrine Pancreas With Needle-Type Glucose Sensor”, The Lancet, 1982, pp. 1129-1131.
  • Shults, M. C., et al., “A Telemetry-Instrumentation System for Monitoring Multiple Subcutaneously Implanted Glucose Sensors”, IEEE Transactions on Biomedical Engineering, vol. 41, No. 10, 1994, pp. 937-942.
  • Sternberg, R., et al., “Study and Development of Multilayer Needle-Type Enzyme-Based Glucose Microsensors”, Biosensors, vol. 4, 1988, pp. 27-40.
  • Thompson, M., et al., “In Vivo Probes: Problems and Perspectives”, Clinical Biochemistry, vol. 19, 1986, pp. 255-261.
  • Turner, A., et al., “Diabetes Mellitus: Biosensors for Research and Management”, Biosensors, vol. 1, 1985, pp. 85-115.
  • Updike, S. J., et al., “Principles of Long-Term Fully Implanted Sensors with Emphasis on Radiotelemetric Monitoring of Blood Glucose from Inside a Subcutaneous Foreign Body Capsule (FBC)”, Biosensors in the Body: Continuous in vivo Monitoring, Chapter 4, 1997, pp. 117-137.
  • Velho, G., et al., “Strategies for Calibrating a Subcutaneous Glucose Sensor”, Biomedica Biochimica Acta, vol. 48, 1989, pp. 957-964.
  • Wilson, G. S., et al., “Progress Toward the Development of an Implantable Sensor for Glucose”, Clinical Chemistry, vol. 38, No. 9, 1992, pp. 1613-1617.
  • PCT Application No. PCT/US2007/082382, International Preliminary Report on Patentability and Written Opinion of the International Searching Authority mailed May 7, 2009.
  • PCT Application No. PCT/US2007/082382, International Search Report and Written Opinion of the International Searching Authority mailed Jun. 24, 2008.
  • U.S. Appl. No. 11/552,935, Advisory Action mailed Jun. 26, 2009.
  • U.S. Appl. No. 11/552,935, Notice of Allowance Aug. 20, 2009.
  • U.S. Appl. No. 11/552,935, Office Action mailed Jun. 25, 2008.
  • U.S. Appl. No. 11/552,935, Office Action mailed Mar. 17, 2009.
  • Maher, “Audio Enhancement using Nonlinear Time-Frequency Filtering”, AES 26th International Conference, 2005, pp. 1-9.
  • Maher, “A method for Extrapolation of Missing Digital Audio Data”, Preprints of Papers Presented at the AES Convention, 1993, pp. 1-19.
  • Georgescu, B., et al., “Real-Time Multimodel Tracking of Myocardium in Echocardiography Using Robust Information Fusion”, Medical Image Computing and Computer-Assisted Intervention, 2004, pp. 777-785.
  • Goldman, J. M., et al., “Masimo Signal Extraction Pulse Oximetry”, Journal of Clinical Monitoring and Computing, vol. 16, No. 7, 2000, pp. 475-483.
  • Whipple, G., “Low Residual Noise Speech Enhancement Utilizing Time-Frequency”, Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, vol. 19, 1994 pp. I5-I8.
  • Wolfe, P. J., et al., “Interpolation of Missing Data Values for Audio Signal Restoration Using a Gabor Regression Model”, 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, 2005, pp. 517-520.
  • Canadian Patent Application No. 2,667,930, Examiner's Report mailed Mar. 2, 2010.
  • Canadian Patent Application No. 2,667,930, Notice of Allowance mailed Oct. 14, 2010.
  • European Patent Application No. 07854382.4, Extended European Search Report mailed Dec. 23, 2009.
  • U.S. Appl. No. 12/506,227, Office Action mailed Oct. 7, 2011.
Patent History
Patent number: 8211016
Type: Grant
Filed: Sep 26, 2008
Date of Patent: Jul 3, 2012
Patent Publication Number: 20090069649
Assignee: Abbott Diabetes Care Inc. (Alameda, CA)
Inventor: Erwin S. Budiman (Fremont, CA)
Primary Examiner: Patricia Mallari
Attorney: Jackson & Co., LLP
Application Number: 12/238,874